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README.md

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+---
+title: Plonk
+emoji: 👀
+colorFrom: yellow
+colorTo: purple
+sdk: gradio
+sdk_version: 3.44.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+![teaser.png](teaser.png)
+# OpenStreetView-5M <br><sub>The Many Roads to Global Visual Geolocation 📍🌍</sub>
+
+OpenStreetView-5M is hosted at [huggingface/datasets/osv5m/osv5m](https://huggingface.co/datasets/osv5m/osv5m)
+Demo for [OpenStreetView-5M: The Many Roads to Global Visual Geolocation](https://imagine.enpc.fr/~guillaume-astruc/osv-5m).  
+
+**First authors:** [Guillaume Astruc](https://gastruc.github.io/), [Nicolas Dufour](https://nicolas-dufour.github.io/), [Ioannis Siglidis](https://imagine.enpc.fr/~siglidii/)  
+**Second authors:** [Constantin Aronssohn](), Nacim Bouia, [Stephanie Fu](https://stephanie-fu.github.io/), [Romain Loiseau](https://romainloiseau.fr/), [Van Nguyen Nguyen](https://nv-nguyen.github.io/), [Charles Raude](https://imagine.enpc.fr/~raudec/), [Elliot Vincent](https://imagine.enpc.fr/~vincente/), Lintao XU, Hongyu Zhou  
+**Last author:** [Loic Landrieu](https://loiclandrieu.com/)  
+**Research Institute:** [Imagine](https://imagine.enpc.fr/), _LIGM, Ecole des Ponts, Univ Gustave Eiffel, CNRS, Marne-la-Vallée, France_  
+
+OpenStreetView-5M is the first large-scale open geolocation benchmark of streetview images.  
+To get a sense of the difficulty of the benchmark, you can play our [demo](https://huggingface.co/spaces/osv5m/plonk).  
+Our dataset was used in an extensive benchmark of which we provide the best model.  
+For more details and results, please check out our [paper](arxiv) and [project page](https://imagine.enpc.fr/~guillaume-astruc/osv-5m).  
+
+### Citing 💫
+
+```bibtex
+@article{osv5m,
+    title = {{OpenStreetView-5M}: {T}he Many Roads to Global Visual Geolocation},
+    author = {Astruc, Guillaume and Dufour, Nicolas and Siglidis, Ioannis
+      and Aronssohn, Constantin and Bouia, Nacim and Fu, Stephanie and Loiseau, Romain
+      and Nguyen, Van Nguyen and Raude, Charles and Vincent, Elliot and Xu, Lintao
+      and Zhou, Hongyu and Landrieu, Loic},
+    journal = {CVPR},
+    year = {2024},
+  }
+```

app.py

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+"""Requires gradio==4.27.0"""
+import io
+import shutil 
+import os
+import json
+import uuid
+import time
+import math
+import datetime
+import numpy as np
+
+from uuid import uuid4
+from PIL import Image
+from math import radians, sin, cos, sqrt, asin, exp
+from os.path import join
+from collections import defaultdict
+from itertools import tee
+
+import matplotlib.style as mplstyle
+mplstyle.use(['fast'])
+import pandas as pd
+
+import gradio as gr
+import reverse_geocoder as rg
+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
+import matplotlib.pyplot as plt
+
+from gradio_folium import Folium
+from geographiclib.geodesic import Geodesic
+from folium import Map, Element, LatLngPopup, Marker, Icon, PolyLine, FeatureGroup
+from folium.map import LayerControl
+from folium.plugins import BeautifyIcon
+from huggingface_hub import CommitScheduler
+
+MPL = False
+IMAGE_FOLDER = './images'
+CSV_FILE = './select.csv'
+BASE_LOCATION = [0, 23]
+RULES = """<h1>OSV-5M (plonk)</h1>
+<center><img width="256" alt="Rotating globe" src="https://upload.wikimedia.org/wikipedia/commons/6/6b/Rotating_globe.gif"></center>
+<h2> Instructions </h2>
+<h3> Click on the map 🗺️ (left) to the location at which you think the image 🖼️ (right) was captured! </h3>
+<h3> Click "Select" to finalize your selection and then "Next" to move to the next image. </h3>
+"""
+css = """
+@font-face {
+  font-family: custom;
+  src: url("/file=custom.ttf");
+}
+
+h1 {
+    text-align: center;
+    display:block;
+    font-family: custom;
+}
+img {
+    text-align: center;
+    display:block;
+}
+h2 {
+    text-align: center;
+    display:block;
+    font-family: custom;
+}
+h3 {
+    text-align: center;
+    display:block;
+    font-family: custom;
+    font-weight: normal;
+}
+"""
+
+space_js = """
+<script src="https://cdn.jsdelivr.net/npm/@rapideditor/[email protected]/dist/country-coder.iife.min.js"></script>
+<script>
+function shortcuts(e) {
+    var event = document.all ? window.event : e;
+    switch (e.target.tagName.toLowerCase()) {
+        case "input":
+        case "textarea":
+        break;
+        default:
+        if (e.key.toLowerCase() == " " && !e.shiftKey) {
+            document.getElementById("latlon_btn").click();
+        }
+    }
+}
+
+function shortcuts_exit(e) {
+    var event = document.all ? window.event : e;
+    switch (e.target.tagName.toLowerCase()) {
+        case "input":
+        case "textarea":
+        break;
+        default:
+        if (e.key.toLowerCase() == "e" && e.shiftKey) {
+            document.getElementById("exit_btn").click();
+        }
+    }
+}
+document.addEventListener('keypress', shortcuts, false);
+document.addEventListener('keypress', shortcuts_exit, false);
+</script>
+"""
+
+def sample_points_along_geodesic(start_lat, start_lon, end_lat, end_lon, min_length_km=2000, segment_length_km=5000, num_samples=None):
+    geod = Geodesic.WGS84
+    distance = geod.Inverse(start_lat, start_lon, end_lat, end_lon)['s12']
+    if distance < min_length_km:
+        return [(start_lat, start_lon), (end_lat, end_lon)]
+
+    if num_samples is None:
+        num_samples = min(int(distance / segment_length_km) + 1, 1000)
+    point_distance = np.linspace(0, distance, num_samples)
+    points = []
+    for pd in point_distance:
+        line = geod.InverseLine(start_lat, start_lon, end_lat, end_lon)
+        g_point = line.Position(pd, Geodesic.STANDARD | Geodesic.LONG_UNROLL)
+        points.append((g_point['lat2'], g_point['lon2']))
+    return points
+
+class GeodesicPolyLine(PolyLine):
+    def __init__(self, locations, min_length_km=2000, segment_length_km=1000, num_samples=None, **kwargs):
+        kwargs1 = dict(min_length_km=min_length_km, segment_length_km=segment_length_km, num_samples=num_samples)
+        assert len(locations) == 2, "A polyline must have at least two locations"
+        start, end = locations
+        geodesic_locs = sample_points_along_geodesic(start[0], start[1], end[0], end[1], **kwargs1)
+        super().__init__(geodesic_locs, **kwargs)
+
+def inject_javascript(folium_map):
+    js = """
+    document.addEventListener('DOMContentLoaded', function() {
+        map_name_1.on('click', function(e) {
+            window.state_data = e.latlng
+        });
+    });
+    """
+    folium_map.get_root().html.add_child(Element(f'<script>{js}</script>'))
+
+def make_map_(name="map_name", id="1"):
+    map = Map(location=BASE_LOCATION, zoom_start=1)
+    map._name, map._id = name, id
+
+    LatLngPopup().add_to(map)
+    inject_javascript(map)
+    return map
+
+def make_map(name="map_name", id="1", height=500):
+    map = make_map_(name, id)
+    fol = Folium(value=map, height=height, visible=False, elem_id='map-fol')
+    return fol
+
+def map_js():
+    return  """
+    (a, textBox) => {
+        const iframeMap = document.getElementById('map-fol').getElementsByTagName('iframe')[0];
+        const latlng = iframeMap.contentWindow.state_data;
+        if (!latlng) { return; }
+        textBox = `${latlng.lat},${latlng.lng}`;
+        document.getElementById('coords-tbox').getElementsByTagName('textarea')[0].value = textBox;
+        var a = countryCoder.iso1A2Code([latlng.lng, latlng.lat]);
+        if (!a) { a = 'nan'; }
+        return [a, `${latlng.lat},${latlng.lng},${a}`];
+    }
+    """
+
+def haversine(lat1, lon1, lat2, lon2):
+    if (lat1 is None) or (lon1 is None) or (lat2 is None) or (lon2 is None):
+        return 0
+    R = 6371  # radius of the earth in km
+    dLat = radians(lat2 - lat1)
+    dLon = radians(lon2 - lon1)
+    a = (
+        sin(dLat / 2.0) ** 2
+        + cos(radians(lat1)) * cos(radians(lat2)) * sin(dLon / 2.0) ** 2
+    )
+    c = 2 * asin(sqrt(a))
+    distance = R * c
+    return distance
+
+def geoscore(d):
+    return 5000 * exp(-d / 1492.7)
+
+def compute_scores(csv_file):
+    df = pd.read_csv(csv_file)
+    if 'accuracy_country' not in df.columns:
+        print('Computing scores... (this may take a while)')
+        geocoders = rg.search([(row.true_lat, row.true_lon) for row in df.itertuples(name='Pandas')])
+        df['city'] = [geocoder['name'] for geocoder in geocoders]
+        df['area'] = [geocoder['admin2'] for geocoder in geocoders]
+        df['region'] = [geocoder['admin1'] for geocoder in geocoders]
+        df['country'] = [geocoder['cc'] for geocoder in geocoders]
+
+        df['city_val'] = df['city'].apply(lambda x: 0 if pd.isna(x) or x == 'nan' else 1)
+        df['area_val'] = df['area'].apply(lambda x: 0 if pd.isna(x) or x == 'nan' else 1)
+        df['region_val'] = df['region'].apply(lambda x: 0 if pd.isna(x) or x == 'nan' else 1)
+        df['country_val'] = df['country'].apply(lambda x: 0 if pd.isna(x) or x == 'nan' else 1)
+
+        df['distance'] = df.apply(lambda row: haversine(row['true_lat'], row['true_lon'], row['pred_lat'], row['pred_lon']), axis=1)
+        df['score'] = df.apply(lambda row: geoscore(row['distance']), axis=1)
+        df['distance_base'] = df.apply(lambda row: haversine(row['true_lat'], row['true_lon'], row['pred_lat_base'], row['pred_lon_base']), axis=1)
+        df['score_base'] = df.apply(lambda row: geoscore(row['distance_base']), axis=1)
+
+        print('Computing geocoding accuracy (base)...')
+        geocoders_base = rg.search([(row.pred_lat_base, row.pred_lon_base) for row in df.itertuples(name='Pandas')])
+        df['pred_city_base'] = [geocoder['name'] for geocoder in geocoders_base]
+        df['pred_area_base'] = [geocoder['admin2'] for geocoder in geocoders_base]
+        df['pred_region_base'] = [geocoder['admin1'] for geocoder in geocoders_base]
+        df['pred_country_base'] = [geocoder['cc'] for geocoder in geocoders_base]
+    
+        df['city_hit_base'] = [df['city'].iloc[i] != 'nan' and df['pred_city_base'].iloc[i] == df['city'].iloc[i] for i in range(len(df))]
+        df['area_hit_base'] = [df['area'].iloc[i] != 'nan' and df['pred_area_base'].iloc[i] == df['area'].iloc[i] for i in range(len(df))]
+        df['region_hit_base'] = [df['region'].iloc[i] != 'nan' and df['pred_region_base'].iloc[i] == df['region'].iloc[i] for i in range(len(df))]
+        df['country_hit_base'] = [df['country'].iloc[i] != 'nan' and df['pred_country_base'].iloc[i] == df['country'].iloc[i] for i in range(len(df))]
+
+        df['accuracy_city_base'] = [(0 if df['city_val'].iloc[:i].sum() == 0 else df['city_hit_base'].iloc[:i].sum()/df['city_val'].iloc[:i].sum())*100 for i in range(len(df))]
+        df['accuracy_area_base'] = [(0 if df['area_val'].iloc[:i].sum() == 0 else df['area_hit_base'].iloc[:i].sum()/df['area_val'].iloc[:i].sum())*100 for i in range(len(df))]
+        df['accuracy_region_base'] = [(0 if df['region_val'].iloc[:i].sum() == 0 else df['region_hit_base'].iloc[:i].sum()/df['region_val'].iloc[:i].sum())*100 for i in range(len(df))]
+        df['accuracy_country_base'] = [(0 if df['country_val'].iloc[:i].sum() == 0 else df['country_hit_base'].iloc[:i].sum()/df['country_val'].iloc[:i].sum())*100 for i in range(len(df))]
+
+        print('Computing geocoding accuracy (best)...')
+        geocoders = rg.search([(row.pred_lat, row.pred_lon) for row in df.itertuples()])
+        df['pred_city'] = [geocoder['name'] for geocoder in geocoders]
+        df['pred_area'] = [geocoder['admin2'] for geocoder in geocoders]
+        df['pred_region'] = [geocoder['admin1'] for geocoder in geocoders]
+        df['pred_country'] = [geocoder['cc'] for geocoder in geocoders]
+    
+        df['city_hit'] = [df['city'].iloc[i] != 'nan' and df['pred_city'].iloc[i] == df['city'].iloc[i] for i in range(len(df))]
+        df['area_hit'] = [df['area'].iloc[i] != 'nan' and df['pred_area'].iloc[i] == df['area'].iloc[i] for i in range(len(df))]
+        df['region_hit'] = [df['region'].iloc[i] != 'nan' and df['pred_region'].iloc[i] == df['region'].iloc[i] for i in range(len(df))]
+        df['country_hit'] = [df['country'].iloc[i] != 'nan' and df['pred_country'].iloc[i] == df['country'].iloc[i] for i in range(len(df))]
+
+        df['accuracy_city'] = [(0 if df['city_val'].iloc[:i].sum() == 0 else df['city_hit'].iloc[:i].sum()/df['city_val'].iloc[:i].sum())*100 for i in range(len(df))]
+        df['accuracy_area'] = [(0 if df['area_val'].iloc[:i].sum() == 0 else df['area_hit'].iloc[:i].sum()/df['area_val'].iloc[:i].sum())*100 for i in range(len(df))]
+        df['accuracy_region'] = [(0 if df['region_val'].iloc[:i].sum() == 0 else df['region_hit'].iloc[:i].sum()/df['region_val'].iloc[:i].sum())*100 for i in range(len(df))]
+        df['accuracy_country'] = [(0 if df['country_val'].iloc[:i].sum() == 0 else df['country_hit'].iloc[:i].sum()/df['country_val'].iloc[:i].sum())*100 for i in range(len(df))]
+        df.to_csv(csv_file, index=False)
+
+
+if __name__ == "__main__":
+    JSON_DATASET_DIR = 'results'
+    scheduler = CommitScheduler(
+        repo_id="osv5m/humeval",
+        repo_type="dataset",
+        folder_path=JSON_DATASET_DIR,
+        path_in_repo=f"raw_data",
+        every=2
+    )
+
+
+class Engine(object):
+    def __init__(self, image_folder, csv_file, mpl=True):
+        self.image_folder = image_folder
+        self.csv_file = csv_file
+        self.load_images_and_coordinates(csv_file)
+          
+        # Initialize the score and distance lists
+        self.index = 0
+        self.stats = defaultdict(list)
+
+        # Create the figure and canvas only once
+        self.fig = plt.Figure(figsize=(10, 6))
+        self.mpl = mpl
+        if mpl:
+            self.ax = self.fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
+
+        self.tag = str(uuid4()) + datetime.datetime.now().strftime("__%Y_%m_%d_%H_%M_%S")
+
+    def load_images_and_coordinates(self, csv_file):
+        # Load the CSV
+        df = pd.read_csv(csv_file)
+
+        # Get the image filenames and their coordinates
+        self.images = [os.path.join(self.image_folder, f"{img_path}.jpg") for img_path in df['id'].tolist()[:]]
+        self.coordinates = df[['true_lon', 'true_lat']].values.tolist()[:]
+
+        # compute the admins
+        self.df = df
+        self.admins = self.df[['city', 'area', 'region', 'country']].values.tolist()[:]
+        self.preds = self.df[['pred_lon', 'pred_lat']].values.tolist()[:]
+
+    def isfinal(self):
+        return self.index == len(self.images)-1
+
+    def load_image(self):
+        if self.index > len(self.images)-1:          
+            self.master.update_idletasks()
+            self.finish()
+
+        self.set_clock()
+        return self.images[self.index], '### ' + str(self.index + 1) + '/' + str(len(self.images))
+
+    def get_figure(self):
+        if self.mpl:
+            img_buf = io.BytesIO()
+            self.fig.savefig(img_buf, format='png', bbox_inches='tight', pad_inches=0, dpi=300)
+            pil = Image.open(img_buf)
+            self.width, self.height = pil.size
+            return pil
+        else:
+            pred_lon, pred_lat, true_lon, true_lat, click_lon, click_lat = self.info
+            map = Map(location=BASE_LOCATION, zoom_start=1)
+            map._name, map._id = 'visu', '1'
+
+            icon_star = BeautifyIcon(
+                icon='star',
+                inner_icon_style='color:red;font-size:30px;',
+                background_color='transparent',
+                border_color='transparent',
+            )
+            feature_group = FeatureGroup(name='Ground Truth')
+            Marker(
+                location=[true_lat, true_lon],
+                popup="True location",
+                icon=icon_star,
+            ).add_to(feature_group)
+            map.add_child(feature_group)
+
+            icon_square = BeautifyIcon(
+                icon_shape='rectangle-dot', 
+                border_color='green', 
+                border_width=10,
+            )
+            feature_group_best = FeatureGroup(name='Best Model')
+            Marker(
+                location=[pred_lat, pred_lon],
+                popup="Best Model",
+                icon=icon_square,
+            ).add_to(feature_group_best)
+            GeodesicPolyLine([[true_lat, true_lon], [pred_lat, pred_lon]], color='green').add_to(feature_group_best)
+            map.add_child(feature_group_best)
+
+            icon_circle = BeautifyIcon(
+                icon_shape='circle-dot', 
+                border_color='blue', 
+                border_width=10,
+            )
+            feature_group_user = FeatureGroup(name='User')
+            Marker(
+                location=[click_lat, click_lon],
+                popup="Human",
+                icon=icon_circle,
+            ).add_to(feature_group_user)
+            GeodesicPolyLine([[true_lat, true_lon], [click_lat, click_lon]], color='blue').add_to(feature_group_user)
+            map.add_child(feature_group_user)
+
+            map.add_child(LayerControl())
+
+            return map
+
+    def set_clock(self):
+        self.time = time.time()
+
+    def get_clock(self):
+        return time.time() - self.time
+
+    def mpl_style(self, pred_lon, pred_lat, true_lon, true_lat, click_lon, click_lat):
+        if self.mpl:
+            self.ax.clear()
+            self.ax.set_global()
+            self.ax.stock_img()
+            self.ax.add_feature(cfeature.COASTLINE)
+            self.ax.add_feature(cfeature.BORDERS, linestyle=':')
+
+            self.ax.plot(pred_lon, pred_lat, 'gv', transform=ccrs.Geodetic(), label='model')
+            self.ax.plot([true_lon, pred_lon], [true_lat, pred_lat], color='green', linewidth=1, transform=ccrs.Geodetic())
+            self.ax.plot(click_lon, click_lat, 'bo', transform=ccrs.Geodetic(), label='user')
+            self.ax.plot([true_lon, click_lon], [true_lat, click_lat], color='blue', linewidth=1, transform=ccrs.Geodetic())
+            self.ax.plot(true_lon, true_lat, 'rx', transform=ccrs.Geodetic(), label='g.t.')
+            legend = self.ax.legend(ncol=3, loc='lower center') #, bbox_to_anchor=(0.5, -0.15), borderaxespad=0.
+            legend.get_frame().set_alpha(None)
+            self.fig.canvas.draw()
+        else:
+            self.info = [pred_lon, pred_lat, true_lon, true_lat, click_lon, click_lat]
+
+
+    def click(self, click_lon, click_lat, country):
+        time_elapsed = self.get_clock()
+        self.stats['times'].append(time_elapsed)
+
+        # convert click_lon, click_lat to lat, lon (given that you have the borders of the image)
+        # click_lon and click_lat is in pixels
+        # lon and lat is in degrees
+        self.stats['clicked_locations'].append((click_lat, click_lon))
+        true_lon, true_lat = self.coordinates[self.index]
+        pred_lon, pred_lat = self.preds[self.index]
+        self.mpl_style(pred_lon, pred_lat, true_lon, true_lat, click_lon, click_lat)
+
+        distance = haversine(true_lat, true_lon, click_lat, click_lon)
+        score = geoscore(distance)
+        self.stats['scores'].append(score)
+        self.stats['distances'].append(distance)
+        self.stats['country'].append(int(self.admins[self.index][3] != 'nan' and country == self.admins[self.index][3]))
+
+        df = pd.DataFrame([self.get_model_average(who) for who in ['user', 'best', 'base']], columns=['who', 'GeoScore', 'Distance', 'Accuracy (country)']).round(2)
+        result_text = (f"### GeoScore: {score:.0f}, distance: {distance:.0f} km")
+
+        self.cache(self.index+1, score, distance, (click_lat, click_lon), time_elapsed)
+        return self.get_figure(), result_text, df
+
+    def next_image(self):
+        # Go to the next image
+        self.index += 1
+        return self.load_image()
+
+    def get_model_average(self, which, all=False):
+        aux, i = [], self.index+1
+        if which == 'user':
+            avg_score = sum(self.stats['scores']) / len(self.stats['scores']) if self.stats['scores'] else 0
+            avg_distance = sum(self.stats['distances']) / len(self.stats['distances']) if self.stats['distances'] else 0
+            avg_country_accuracy = (0 if self.df['country_val'].iloc[:i].sum() == 0 else sum(self.stats['country'])/self.df['country_val'].iloc[:i].sum())*100
+            if all:
+                avg_city_accuracy = (0 if self.df['city_val'].iloc[:i].sum() == 0 else sum(self.stats['city'])/self.df['city_val'].iloc[:i].sum())*100
+                avg_area_accuracy = (0 if self.df['area_val'].iloc[:i].sum() == 0 else sum(self.stats['area'])/self.df['area_val'].iloc[:i].sum())*100
+                avg_region_accuracy = (0 if self.df['region_val'].iloc[:i].sum() == 0 else sum(self.stats['region'])/self.df['region_val'].iloc[:i].sum())*100
+                aux = [avg_city_accuracy, avg_area_accuracy, avg_region_accuracy]
+        elif which == 'base':
+            avg_score = np.mean(self.df[['score_base']].iloc[:i])
+            avg_distance = np.mean(self.df[['distance_base']].iloc[:i])
+            avg_country_accuracy = self.df['accuracy_country_base'].iloc[i]
+            if all:
+                aux = [self.df['accuracy_city_base'].iloc[i], self.df['accuracy_area_base'].iloc[i], self.df['accuracy_region_base'].iloc[i]]
+        elif which == 'best':
+            avg_score = np.mean(self.df[['score']].iloc[:i])
+            avg_distance = np.mean(self.df[['distance']].iloc[:i])
+            avg_country_accuracy = self.df['accuracy_country'].iloc[i]
+            if all:
+                aux = [self.df['accuracy_city_base'].iloc[i], self.df['accuracy_area_base'].iloc[i], self.df['accuracy_region_base'].iloc[i]]
+        return [which, avg_score, avg_distance, avg_country_accuracy] + aux
+
+    def update_average_display(self):
+        # Calculate the average values
+        avg_score = sum(self.stats['scores']) / len(self.stats['scores']) if self.stats['scores'] else 0
+        avg_distance = sum(self.stats['distances']) / len(self.stats['distances']) if self.stats['distances'] else 0
+
+        # Update the text box
+        return f"GeoScore: {avg_score:.0f}, Distance: {avg_distance:.0f} km"
+    
+    def finish(self):
+        clicks = rg.search(self.stats['clicked_locations'])
+        self.stats['city'] = [(int(self.admins[self.index][0] != 'nan' and click['name'] == self.admins[self.index][0])) for click in clicks]
+        self.stats['area'] = [(int(self.admins[self.index][1] != 'nan' and click['admin2'] == self.admins[self.index][1])) for click in clicks]
+        self.stats['region'] = [(int(self.admins[self.index][2] != 'nan' and click['admin1'] == self.admins[self.index][2])) for click in clicks]
+        
+        df = pd.DataFrame([self.get_model_average(who, True) for who in ['user', 'best', 'base']], columns=['who', 'GeoScore', 'Distance', 'Accuracy (country)', 'Accuracy (city)', 'Accuracy (area)', 'Accuracy (region)'])
+        return df
+        
+    # Function to save the game state
+    def cache(self, index, score, distance, location, time_elapsed):
+        with scheduler.lock:
+            os.makedirs(join(JSON_DATASET_DIR, self.tag), exist_ok=True)
+            with open(join(JSON_DATASET_DIR, self.tag, f'{index}.json'), 'w') as f:
+                json.dump({"lat": location[0], "lon": location[1], "time": time_elapsed, "user": self.tag}, f)
+                f.write('\n')
+
+
+if __name__ == "__main__":
+    # login with the key from secret
+    if 'csv' in os.environ:
+        csv_str = os.environ['csv']
+        with open(CSV_FILE, 'w') as f:
+            f.write(csv_str)
+    
+    compute_scores(CSV_FILE)
+    import gradio as gr
+    def click(state, coords):
+        if coords == '-1' or state['clicked']:
+            return gr.update(), gr.update(), gr.update(), gr.update()
+        lat, lon, country = coords.split(',')
+        state['clicked'] = True
+        image, text, df = state['engine'].click(float(lon), float(lat), country)
+        df = df.sort_values(by='GeoScore', ascending=False)
+        return gr.update(visible=False), gr.update(value=image, visible=True), gr.update(value=text, visible=True), gr.update(value=df, visible=True)
+
+    def exit_(state):
+        if state['engine'].index > 0:
+            df = state['engine'].finish()
+            return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(value='', visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(value=df, visible=True), gr.update(value="-1", visible=False), gr.update(value="<h1 style='margin-top: 4em;'> Your stats on OSV-5M🌍 </h1>", visible=True), gr.update(value="<h3 style='margin-top: 1em;'>Thanks for playing ❤️</h3>", visible=True), gr.update(visible=False)
+        else:
+            return gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update()
+
+    def next_(state):
+        if state['clicked']:
+            if state['engine'].isfinal():
+                return exit_(state)
+            else:
+                image, text = state['engine'].next_image()
+                state['clicked'] = False
+                return gr.update(value=make_map_(), visible=True), gr.update(visible=False), gr.update(value=image), gr.update(value=text), gr.update(visible=False), gr.update(), gr.update(visible=False), gr.update(value="-1"), gr.update(), gr.update(), gr.update()
+        else:
+            return gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update()
+
+    def start(state):
+        # create a unique random temporary name under CACHE_DIR
+        # generate random hex and make sure it doesn't exist under CACHE_DIR
+        state['engine'] = Engine(IMAGE_FOLDER, CSV_FILE, MPL)
+        state['clicked'] = False
+        image, text = state['engine'].load_image()
+
+        return (
+            gr.update(visible=True),
+            gr.update(visible=False),
+            gr.update(value=image, visible=True),
+            gr.update(value=text, visible=True),
+            gr.update(visible=True),
+            gr.update(visible=True),
+            gr.update(value="<h1>OSV-5M (plonk)</h1>"),
+            gr.update(visible=False),
+            gr.update(visible=False),
+            gr.update(value="-1"),
+            gr.update(visible=True),
+        )
+
+    with gr.Blocks(css=css, head=space_js) as demo:
+        state = gr.State({})
+        rules = gr.Markdown(RULES, visible=True)
+
+        exit_button = gr.Button("Exit", visible=False, elem_id='exit_btn')
+        start_button = gr.Button("Start", visible=True)
+        with gr.Row():
+            map_ = make_map(height=512)
+            if MPL:
+                results = gr.Image(label='Results', visible=False)
+            else:
+                results = Folium(height=512, visible=False)
+            image_ = gr.Image(label='Image', visible=False, height=512)
+
+        with gr.Row():
+            text = gr.Markdown("", visible=False)
+            text_count = gr.Markdown("", visible=False)
+
+        with gr.Row():
+            select_button = gr.Button("Select", elem_id='latlon_btn', visible=False)
+            next_button = gr.Button("Next", visible=False, elem_id='next')
+        perf = gr.Dataframe(value=None, visible=False)
+        text_end = gr.Markdown("", visible=False)
+    
+        coords = gr.Textbox(value="-1", label="Latitude, Longitude", visible=False, elem_id='coords-tbox')
+        start_button.click(start, inputs=[state], outputs=[map_, results, image_, text_count, text, next_button, rules, state, start_button, coords, select_button])
+        select_button.click(click, inputs=[state, coords], outputs=[map_, results, text, perf], js=map_js())
+        next_button.click(next_, inputs=[state], outputs=[map_, results, image_, text_count, text, next_button, perf, coords, rules, text_end, select_button])
+        exit_button.click(exit_, inputs=[state], outputs=[map_, results, image_, text_count, text, next_button, perf, coords, rules, text_end, select_button])
+
+    demo.launch(allowed_paths=["custom.ttf"], debug=True)